The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
techtimes

The Role of Assumptions in Statistical Model Selection

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...
National Academies of Sciences%2c Engineering%2c and Medicine

Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification

NOTICE: The project that is the subject of this report was approved by the Governing Board of the National Research Council, whose members are drawn from the councils of the National Academy of ...
Business Wire

Beaconcure Announces Launch of Automated Validation of Statistical Analysis Outputs Against ADaM Datasets

BOSTON--(BUSINESS WIRE)--Beaconcure, a leader in AI-enabled clinical data validation and automation, has launched its new Verify capability to automate validation of statistical analysis outputs ...
The American Journal of Managed Care

Validation of the FINE-CKD Model for Future Health Technology Assessments for Finerenone in Patients With Chronic Kidney Disease And Type 2 Diabetes

BACKGROUND: The FINE-CKD model was developed to estimate the cost-effectiveness of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). OBJECTIVE: To perform internal ...